Syllabus academic year 2011/2012
(Created 2011-09-01.)
COMPUTER VISIONFMA270
Credits: 6. Grading scale: TH. Cycle: A (Second Cycle). Main field: Technology. Language of instruction: The course will be given in English on demand. FMA270 overlaps following cours/es: FMA271. Optional for: C4, D4, D4bg, E4, E4bg, F4, F4bg, Pi4, Pi4ssr. Course coordinator: Director of Studies Anders Holst, Anders.Holst@math.lth.se, Mathematics. Recommended prerequisits: FMAF05 Systems and Transforms, or equivalent (for example FMAF10). Assessment: Compulsory computer exercises and assignments. Approved results on these are enough to pass the course. To get a higher grade it is necessary to pass a written and an oral test . Home page: http://www.maths.lth.se/matematiklth/vision/datorseende/datorseende.html.

Aim
The aim of the course is to give an overview of the theory and practically useful methods in computer vision, with applications within e.g. vision systems, non-invasive measurements and augmented reality. In addition the aim is to make the student develop his or her ability in problem solving, with and without a computer, using mathematical tools taken from many areas of the mathematical sciences, in particular geometry, optimization, mathematical statistics, invariant theory and transform theory.

Knowledge and understanding
For a passing grade the student must

be able to clearly explain and use basic concepts in computer vision, in particular regarding projective geometry, camera modelling, stereo vision, recognition and structure and motion problems.

be able to describe and give an informal explanation of the mathematical theory behind some central algorithms in computer vision (the least squares method, Newton based optimization and stochastic methods).

Skills and abilities
For a passing grade the student must

in an engineering manner be able to use computer packages to independently solve problems in computer vision.

be able to show good ability to independently identify problems which can be solved with methods from computer vision, and be able to choose an appropriate method.

be able to independently apply basic methods in computer vision to problems which are relevant in industrial applications or research.

with proper terminology, in a well-structured way and with clear logic, be able to explain the solution to a problem in computer vision.

Contents
Projective geometry. Geometric transformations. Modelling cameras. Stereo vision. Photogrammetry. Recognition. 3D-modeling. Geometry of surfaces and their silhouettes. Visualisation.

Literature
R. Szeliski, Computer Vision: Algorithms and Applications, Springer Verlag 2010. ISBN: 9781848829343